An agent-based memetic algorithm (AMA) for solving constrained optimization problems

Abu S.S.M.Barkat Ullah; Ruhul Sarker; David Cormfort; Chris Lokan

doi:10.1109/CEC.2007.4424579

An agent-based memetic algorithm (AMA) for solving constrained optimization problems

Abu S.S.M.Barkat Ullah, Ruhul Sarker, David Cormfort, Chris Lokan

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

28 Citations (Scopus)

Abstract

In recent years, memetic algorithms (MAs) have been proposed to enhance the performance of evolutionary algorithms by incorporating local search techniques with evolutionary algorithms' global search ability, and applied successfully to solve different type of optimization problems. This paper proposes a new memetic algorithm and then introduces an agent-based memetic algorithm (AMA), for the first time, to further enhance the ability of MA in solving constrained optimization problems. In a lattice-like environment, each of the agents represents a candidate solution of the problem. The agents are able to sense and act on the society, and their performances i.e. fitness of the solution improves through co-evolutionary adaptation of society with the individual learning of the agents. The proposed algorithm is tested on 13 benchmark problems and the experimental results show promising performance.

Original language	English
Title of host publication	2007 IEEE Congress on Evolutionary Computation, CEC 2007
Editors	Kay Chen Tan, Jian Xin Xu, Dipti Srinivasan, Lipo Wang
Place of Publication	United States
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	999-1006
Number of pages	8
ISBN (Print)	1424413400, 9781424413409
DOIs	https://doi.org/10.1109/CEC.2007.4424579
Publication status	Published - 1 Dec 2007
Externally published	Yes
Event	2007 IEEE Congress on Evolutionary Computation, CEC 2007 - , Singapore Duration: 25 Sept 2007 → 28 Sept 2007

Publication series

Name	2007 IEEE Congress on Evolutionary Computation, CEC 2007

Conference

Conference	2007 IEEE Congress on Evolutionary Computation, CEC 2007
Country/Territory	Singapore
Period	25/09/07 → 28/09/07

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10.1109/CEC.2007.4424579

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Ullah, A. S. S. M. B., Sarker, R., Cormfort, D., & Lokan, C. (2007). An agent-based memetic algorithm (AMA) for solving constrained optimization problems. In K. C. Tan, J. X. Xu, D. Srinivasan, & L. Wang (Eds.), 2007 IEEE Congress on Evolutionary Computation, CEC 2007 (pp. 999-1006). [4424579] (2007 IEEE Congress on Evolutionary Computation, CEC 2007). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/CEC.2007.4424579

Ullah, Abu S.S.M.Barkat ; Sarker, Ruhul ; Cormfort, David et al. / An agent-based memetic algorithm (AMA) for solving constrained optimization problems. 2007 IEEE Congress on Evolutionary Computation, CEC 2007. editor / Kay Chen Tan ; Jian Xin Xu ; Dipti Srinivasan ; Lipo Wang. United States : IEEE, Institute of Electrical and Electronics Engineers, 2007. pp. 999-1006 (2007 IEEE Congress on Evolutionary Computation, CEC 2007).

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title = "An agent-based memetic algorithm (AMA) for solving constrained optimization problems",

abstract = "In recent years, memetic algorithms (MAs) have been proposed to enhance the performance of evolutionary algorithms by incorporating local search techniques with evolutionary algorithms' global search ability, and applied successfully to solve different type of optimization problems. This paper proposes a new memetic algorithm and then introduces an agent-based memetic algorithm (AMA), for the first time, to further enhance the ability of MA in solving constrained optimization problems. In a lattice-like environment, each of the agents represents a candidate solution of the problem. The agents are able to sense and act on the society, and their performances i.e. fitness of the solution improves through co-evolutionary adaptation of society with the individual learning of the agents. The proposed algorithm is tested on 13 benchmark problems and the experimental results show promising performance.",

keywords = "Agent-based systems, Constrained optimization, Evolutionary algorithms, Genetic algorithms, Memetic algorithms, Nonlinear programming",

author = "Ullah, {Abu S.S.M.Barkat} and Ruhul Sarker and David Cormfort and Chris Lokan",

year = "2007",

month = dec,

day = "1",

doi = "10.1109/CEC.2007.4424579",

language = "English",

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series = "2007 IEEE Congress on Evolutionary Computation, CEC 2007",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

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editor = "Tan, {Kay Chen} and Xu, {Jian Xin} and Dipti Srinivasan and Lipo Wang",

booktitle = "2007 IEEE Congress on Evolutionary Computation, CEC 2007",

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Ullah, ASSMB, Sarker, R, Cormfort, D & Lokan, C 2007, An agent-based memetic algorithm (AMA) for solving constrained optimization problems. in KC Tan, JX Xu, D Srinivasan & L Wang (eds), 2007 IEEE Congress on Evolutionary Computation, CEC 2007., 4424579, 2007 IEEE Congress on Evolutionary Computation, CEC 2007, IEEE, Institute of Electrical and Electronics Engineers, United States, pp. 999-1006, 2007 IEEE Congress on Evolutionary Computation, CEC 2007, Singapore, 25/09/07. https://doi.org/10.1109/CEC.2007.4424579

An agent-based memetic algorithm (AMA) for solving constrained optimization problems. / Ullah, Abu S.S.M.Barkat; Sarker, Ruhul; Cormfort, David et al.
2007 IEEE Congress on Evolutionary Computation, CEC 2007. ed. / Kay Chen Tan; Jian Xin Xu; Dipti Srinivasan; Lipo Wang. United States: IEEE, Institute of Electrical and Electronics Engineers, 2007. p. 999-1006 4424579 (2007 IEEE Congress on Evolutionary Computation, CEC 2007).

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

TY - GEN

T1 - An agent-based memetic algorithm (AMA) for solving constrained optimization problems

AU - Ullah, Abu S.S.M.Barkat

AU - Sarker, Ruhul

AU - Cormfort, David

AU - Lokan, Chris

PY - 2007/12/1

Y1 - 2007/12/1

N2 - In recent years, memetic algorithms (MAs) have been proposed to enhance the performance of evolutionary algorithms by incorporating local search techniques with evolutionary algorithms' global search ability, and applied successfully to solve different type of optimization problems. This paper proposes a new memetic algorithm and then introduces an agent-based memetic algorithm (AMA), for the first time, to further enhance the ability of MA in solving constrained optimization problems. In a lattice-like environment, each of the agents represents a candidate solution of the problem. The agents are able to sense and act on the society, and their performances i.e. fitness of the solution improves through co-evolutionary adaptation of society with the individual learning of the agents. The proposed algorithm is tested on 13 benchmark problems and the experimental results show promising performance.

AB - In recent years, memetic algorithms (MAs) have been proposed to enhance the performance of evolutionary algorithms by incorporating local search techniques with evolutionary algorithms' global search ability, and applied successfully to solve different type of optimization problems. This paper proposes a new memetic algorithm and then introduces an agent-based memetic algorithm (AMA), for the first time, to further enhance the ability of MA in solving constrained optimization problems. In a lattice-like environment, each of the agents represents a candidate solution of the problem. The agents are able to sense and act on the society, and their performances i.e. fitness of the solution improves through co-evolutionary adaptation of society with the individual learning of the agents. The proposed algorithm is tested on 13 benchmark problems and the experimental results show promising performance.

KW - Agent-based systems

KW - Constrained optimization

KW - Evolutionary algorithms

KW - Genetic algorithms

KW - Memetic algorithms

KW - Nonlinear programming

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SN - 1424413400

SN - 9781424413409

T3 - 2007 IEEE Congress on Evolutionary Computation, CEC 2007

SP - 999

EP - 1006

BT - 2007 IEEE Congress on Evolutionary Computation, CEC 2007

A2 - Tan, Kay Chen

A2 - Xu, Jian Xin

A2 - Srinivasan, Dipti

A2 - Wang, Lipo

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - United States

T2 - 2007 IEEE Congress on Evolutionary Computation, CEC 2007

Y2 - 25 September 2007 through 28 September 2007

ER -

Ullah ASSMB, Sarker R, Cormfort D, Lokan C. An agent-based memetic algorithm (AMA) for solving constrained optimization problems. In Tan KC, Xu JX, Srinivasan D, Wang L, editors, 2007 IEEE Congress on Evolutionary Computation, CEC 2007. United States: IEEE, Institute of Electrical and Electronics Engineers. 2007. p. 999-1006. 4424579. (2007 IEEE Congress on Evolutionary Computation, CEC 2007). doi: 10.1109/CEC.2007.4424579

An agent-based memetic algorithm (AMA) for solving constrained optimization problems

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